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Talent_Distribution
Friday, June 19, 2009
I’m just looking at Rally’s WAR database, and here are his totals of WAR for the ten year period 1999-2008, as average per team per year:
15.8 Non-pitcher WAR
8.2 Starter WAR
3.4 Relief WAR
---------------
27.4 Total WAR
I estimated the breakdown of the last two, but combined it is 11.6. So, he allocated 57.7% to non-pitchers and 42.3% to pitchers, which is right around where I have it. The starter-relief split is 70/30, which is too much for relievers (compared to my method). It’s possible that my way to split up his WAR for the swingmen was too primitive to make this 70/30 claim valid.
At 27.4 WAR, he’s putting the replacement-level team at 81-27.4 = 53.6 wins, or .330 win%, which is a bit higher than what I use. 0.330 is justifiable.
The 8.2 WAR for starters sets the replacement level at .420 for starters. I can’t do the same calculation for relievers because of the leverage aspect. The 15.8 nonpitcher WAR sets the replacement level at .402 for the team of nonpitchers, per game.
I use .380 for starting pitchers and for nonpitchers. His relief WAR is likely consistent with my levels.
Friday, June 12, 2009
Craig Brown points out:
This season however, Tejada has cut his strikeouts like never before. Through Wednesday, he’s struck out just 15 times in 230 at bats. At 6.5 percent, he’s whiffing at the lowest rate of his career. And his 15.6 AB/K ratio is the best in the league.
And while he’s not striking out, he’s not taking the free pass, either. Tejada has drawn just six walks (one intentional) in his 242 plate appearances. Patience hasn’t been his strength over his career - his highest walk rate was in 2000 when he drew a career high 66 walks and strolled to first 9.8 percent of the time.
This year, Tejada is swinging as aggressive as ever. He’s seeing just 3.3 pitches per plate appearance - the 12th lowest total among all players with at least 150 plate appearances. He’s hacking right out of the box. Tejada is swinging at the first pitch 27 percent of the time.
What is most intriguing is that a (former?) power hitter like Tejada is anywhere near a list like this. At the bottom is just downright shocking.
...
He’s certainly performing at a high level and it’s been going on for most of the season. But at some point, he’ll have to tone things down or his free swinging ways catch up. Pitchers are undoubtedly aware of this. As the season continues, look for Tejada to see fewer strikes, forcing him to be patient. He’s been around the block a few times, so it will be interesting to see how he adjusts.
Tuesday, June 09, 2009
This exchange between Poz and Bill James is more like it.
At the same time, the awareness of “doing whatever you can to get on base”, among some hitters and some teams, was much more “naked” than it is now. In modern baseball it is considered bad form to specialize in walking, and nobody really does. But if you go back to 1900, 1910, 1920, there were a certain number of players—one or two on each team—who very clearly understood that their job was to get on base any way they could for the big hitters on the team. These people walked 115 times a year in large part BECAUSE nobody was paying attention to how often they walked.
Starting in 1901:
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Tuesday, May 26, 2009
To catch or not to catch:
Through this past Sunday, Inge’s career batting line while playing catcher has been .199/.260/.330, and while at third base it’s been .257/.329/.433.
Me, from THT09:
Another interesting finding is that the players who were considered catchers but were moved to another position later on were really good hitters. Those 91% who remained at catcher were -9 runs per 650 PA through age 28, and -10 from age 29 onwards. The other 9% were zero runs through age 28 (i.e. average) and +10 runs from age 29 onwards. It is possible that these players, freed from their catcher shackles, went on to be the good hitters they always were.
This is why we need to consider Catcher as a separate pool when applying the positional adjustments.
Friday, May 22, 2009
We have two recent articles on Micah Owings. One from Jeremy, and another by Colin. The question is what to do with Micah Owings: 100% nonpitcher, platoon between OF/pitcher, mostly pitcher + occaisional PH.
In 147 MLB PA, he has a slash line of .314/.345/.569. In 71 MiLB PA, he has a slash line of .354/ .368/ .492. He’s been used as a PH five times, which means the manager thinks he’s something other than your typical pitcher.
As a pitcher, he is basically a slightly below-average starter, with a “neutral” win % of around .465 in a “neutral” league (or +.085 wins above replacement). That makes him your standard #4 starter. Such a pitcher, if we give him 180 IP (20 full games) would be a 1.7 WAR pitcher. On top of that, he gets to bat, as a pitcher, and as a PH. If you give him 50 PA as a pitcher, and figure he’s +.015 wins per PA above the average pitcher-as-a-hitter, that’s another +0.75 wins… nothing to sneeze at, that’s for sure. If we give him 10 PH at bats, and presume, he’s +.001 wins per PA above the average PH, that’s another +.01 wins (essentially irrelevant). So, we’ve got ourselves a 2.46 WAR players, as a pitcher. That makes him an $11MM - $12MM player.
That’s quite a starting point to compare him against. If you make him a full-time hitter, let’s say he’d be a +1 win hitter per 162 games. Let’s figure he’d be an average-fielding corner outfielder. And, I presume, a below-average baserunner. That makes him a league average player. Add in the 2.25 WAR per 162 G, and give him 80% playing time, and you’ve got yourself a 1.8 WAR player. With a huge level of uncertainty.
So, no, it makes it virtually impossible to turn him into a hitter-only player.
But, what about a part-timer? I would suspect that he’d become both a worse hitter and worse pitcher if that were to happen. Let’s say that he goes from being a .465 pitcher to a .440 pitcher. Let’s also say that instead of 20 full games, he’ll only be able to pitch 15 full games (135 IP). That makes him a +0.9 WAR pitcher. He gets 40 PA as a pitcher, which means adding +0.6 wins. As a hitter, let’s say he’s now a +0.5 win hitter per 162 games. And we make him play half the time. That makes him a +0.9 WAR hitter. Add it up, and we have a 2.4 WAR hybrid. With a huge level of uncertainty.
So, this makes more sense, but it’s a break-even proposition, but with huge risks both ways.
As it stands, through simple intuition, wisdom, and experience, Micah Owings and MLB have found the optimal setting in how to use Micah Owings: as a starting pitcher.
Friday, May 15, 2009
Here are their numbers in 1996, as well as how the draft went:
Grouping # Total WAR Avg /season
College Pitcher 8 31.3 0.652
HS Pitcher 13 23.5 0.301
College Hitter 5 16.1 0.536
HS Hitter 9 22.4 0.414
First off, the negative WAR should be reduced to zero. Secondly, we would LIKE to see the WAR per player per the same for every subgroup. That would imply we have equilibrium. Obviously, we can’t make any conclusion based on one year. But, if you do this over a long enough time period, you get to see how well MLB has done in choosing between HS, College, Pitchers, and hitters. Here’s an example of what I mean.
Monday, May 11, 2009
Obviously a highly-selective sample, but Rally gives us the list.
Wednesday, April 29, 2009
A good job by devil_fingers on seeing what kind of performance is required, in order to justify the contracts paid out to DH.
A little technical note: the 40/60/80 should apply to the salary above the min 400K. Also, for the pre-arb players, you really shouldn’t bother trying to figure out what his real value is, because the correlation is rather weak in terms of salary and talent, since all the pre-arb players barely make anything about the 400K minimum.
In any case, when you look at the chart, compare the “xwOBA” figure he has (which is the wOBA the hitter needs to get, in order to justify his contract) to the Marcel (or Chone) forecast column. Just eyeballing it, and it looks like a decent match. We get a decent match because the basic idea behind the WAR process for DH works fairly well: the replacement-level DH is a league average hitter. Teams are paying with that in mind.
Monday, April 20, 2009
A human is a human. Isn’t it sad that I actually have to say it? And isn’t it said that someone has to say this:
Tim Purpura, COO of Minor League Baseball, went as far to indicate that he would have been willing to sign a little person to draw walks if major league baseball would have allowed during his tenure as general manager of the Houston Astros.
As Justin said:
It’s probably mostly just a publicity stunt by an indepedent-league team. But the thing is, I can’t work out any reason to be directly opposed to this idea that isn’t blatantly prejudicial.
Whether it’s Manon Rheaume or Hayley Wickenheiser or Eddie Gaedel or Jackie Robinson, it should be irrelevant.
How good would a player who would only get walks have to be? A walk is worth about +.030 wins and an out is -.027 wins. If you can get a .475 OBP, you’d be a league-average hitter, which, for a guy who can’t field (presumably) would be the replacment-level. If we’re looking for a 1 WAR per 162G (700PA) player as our threshhold, our guy needs to have a .500 OBP. He would be an average player if he could get a .530 OBP.
Seeing that MLB pitchers throw ball 4 on 3-0 counts 35% of the time, I can certainly believe that MLB pitchers may have a tough time with the pin-point control they need.
A short American playing in MLB should be as much a non-story as a tall Chinese playing in the NBA.
Friday, April 17, 2009
Greg sent me an email:
Thought you might like to hear about something I’ve been following for the first week of the season. I began wondering at the large number of long home runs being hit in the first two full days of the season, and started watching the numbers closely. The distance of the home runs being hit this year (the true distance, i.e where they actually land, as well as the standard distance, which factors out weather and altitude) is significantly higher than last year, with the average standard distance being 8.5 feet longer this year than last.
You may be wondering about sample sizes, and of course I took that into account. I used a 2-sample T-test on the 2009 and 2008 full season data, and got this:
Two-Sample T-Test and CI: 2009, 2008
Two-sample T for 2009 vs 2008
N Mean StDev SE Mean
2009 199 399.8 27.8 2.0
2008 4820 391.3 25.4 0.37
Difference = mu (2009) - mu (2008)
Estimate for difference: 8.49
95% CI for difference: (4.54, 12.45)
T-Test of difference = 0 (vs not =)
: T-Value = 4.23 P-Value = 0.000 DF = 211
The p-value actually works out to 0.0000341, which is a very strong indicator that something is making 2009 home runs fly farther than 2008 home runs, in isolation of the weather, and to me that implicates the ball. In the course of observing all the homers, I have also heard lots of comments from announcers who were surprised at how far the ball had carried.
When I look at only April, 2008, I get a p-value of 0.01, so I don’t think it’s just some sort of calendar thing here. I’ve done the same comparison to 2007, 2006, and the month of April for each of those years, and all indications agree that the difference is significant.
So, you might want to dust off your calculations from that “Changes in HR Rates from the Retrosheet Years” article and see what you get. Looks like a big year for homers, and so far the actual rate of 2.14 HR per game (in April!) doesn’t contradict that…
I’ll only be able to report my results at the end of the year. Drastic single-year changes only happens when you have a catalyst, as discussed in my article on the subject.
***
These are Greg’s images from post 48:
Monday, April 06, 2009
Q & A with JC:
Q: You have developed Marginal Revenue Products for players, while Fangraphs has a very different system—with very different results—to evaluate a player’s offensive and defensive worth....
A: Yeah, I’m not a fan of the popular online approaches. For one, replacement players aren’t worth league minimum any more than a Starbucks gift card from your grandma makes coffee free. Like the gift card, players have value beyond what you have pay to get the good. Also, the revenue-wins relationship is non-linear, increasing at an increasing rate. Valuing players is difficult. ...
Friday, April 03, 2009
A reader was asking me about starting a baseball league from scratch. All you have is:
1. A population of 5000 nonpitchers
2. They have a mean offense runs per PA of 0, with one SD = .150 per PA
3. They have a mean fielding runs per PA of 0, with one SD = .075 per PA (presumes 75% of PA are BIP)
As I told him:
I meant that if you take the 5000 players in pro ball, the range in fielding would come in at +/- .10 runs per BIP, regardless of position. Or more accurate, on average.
So, the range might be +/- .15 runs per BIP at SS, +/- .13 at CF, +/- .12 at 3B, 2B, etc, etc, etc +/- .05 at 1B, etc, etc, etc. So that, on average it would be +/-.10.
On average, I’m saying that it’s +/-.15 runs per PA as a batter, and +/- .10 runs per BIP as a fielder. And since BIP makes up 75% of the PA, then the range is twice as wide hitting-wise than fielding-wise.
The plan therefore is to be able to find the right balance in terms of distributing your talent across positions. Basically, given these conditions, how much hitting and fielding talent should you expect to find in MLB at each position? I guess I should have also noted that we have a handedness constraint at the three IF positions, and we’d have to accept a constraint at C.
The question being asked is if my assumptions are valid: what kind of distribution of hitting and fielding talent should we expect to exist among these 5000 players?
Thursday, April 02, 2009
Cool site, just what the doctored ordered. The team links are on the right-hand side.
I discovered this site in trying to find out why Padres fans can’t nominate enough innings for their pitchers, as well as why is Dan Meyer (Marlins) getting so much action on the high-end.
I remember a long while back looking for players who transition from a career of OF to the IF. It was pretty tough, but the one recent guy I found was Melvin Mora. It will be interested to see if Skip can make the transition. He seems to be an all-round solid fielder according to Cardinals fans. His top comps are littered with middle infielders.
And here’s a related blog post from the same paper.
(Hat tip: Mike)
Tuesday, March 24, 2009
Patriot gives us his view of others’ views:
If Japanese baseball was superior, then we would rightfully expect players from Japan to perform better in the US than they did in Japan, and American refugees to perform worse. But of course the opposite is true; the evidence suggests that the NPB is a strong league, yet clearly inferior to MLB. Yet so many want to chuck the many thousands of PAs and innings available for comparison in favor of a few games played over a three-year span.
If small sample size tournaments are your thing, though, how about the Olympics? The Japanese Olympic team was not as strong as the Japanese WBC team--it obviously featured no major league players, but every player was with an NPB organization and it included multiple players on the WBC team--including Yu Darvish, Toshiya Sugiuchi, Atsunori Inaba, Norichika Aoki, and Hiroyuki Nakajima. This team lost twice to the United States’ collection of minor leaguers and one college phenom, including once in the bronze medal game.
Monday, March 23, 2009
Having grownup watching Patrick Roy, who won the Stanley Cup in his rookie season as playoff MVP, and dominated the NHL goalie scene as well as spur on a whole generation of Quebec goalies to make it to the NHL, it is very easy for me to disagree with Tom as ranking Patrick #1, and go with Dominik Hasek as the best goalie of that time period.
Both goalies were born in the same year (1965), and while Roy made his mark his first year (1985/86), Hasek set the NHL on fire in 1993/94. That is an 8-year headstart for Roy (6 if you want to include Hasek’s two backup years). Hasek however, in those missing years, was Czech goalie of the year for 5 straight years (1986-1990), and PLAYER of the year for 3 of those years, while being in the Canada Cup as his team’s starting goalie in 1987. (This to go along with his five goalie of the year and 2 player of the year in the NHL.)
This is a Bobby Orr situation in reverse. While Bobby Orr is recognized as the greatest defenseman of all time (and will continue to be for at least the next decade, even in the face of Niklas Lidstrom), his career was over at age 26. Hasek’s NHL career started in his late 20s, and so, he didn’t have the “counting” stats (in the NHL) to build on.
But, as I’ve said in the past, referring to Koufax and Pedro and Edgar, I am interested in Observed Performance Inferring True Talent (OPITT). For hockey fans outside of Detroit who see Bobby Orr as the clear #1 defenseman, and a clear top 5 player of all time, this is exactly how hockey fans (implicitly) think as well. There’s no longevity v peak discussion in hockey.
I believe this is true in most sports, and only not true in baseball because baseball has too many numbers for us to consider.
Friday, March 20, 2009
Apparently, this isn’t clear as to how not to apportion credit:
Ok, now let me tell you why this splitting-issue is not the right way to proceed. And I’m going to use hockey as an example. In hockey, everyone on the ice for the scoring team gets a “plus 1”, and the players on the ice for the opposing team gets a “minus 1”. This means that every goal has five pluses and five minuses. If we follow the logic in this paper, this would entail taking this one goal, and somehow splitting it up among the players on the ice. Perhaps giving the five guys on the scoring team a total of +.5 goals, and the give guys on the opposing team a total of -.5. And then, among the scoring team, deciding who gets the share of the +.5, like maybe +.25 for the goal scorer, +.10 for the playmaker, and +.05 for the other guys on the ice.
Assume you have a player, which I’ll call Obby Borr. He’s a +120 for the Bruins, and when he’s not on the ice, his teammates are +0. Also assume that Borr plays with everyone on the team. If you proceed with a “splitting” arrangement, Borr will end up being credited for something like +60 goals, if he’s lucky. Likely, under the splitting-system, he’ll be even lower. However, in my system, he gets +120.
You see, Borr plus his teammates is +120. All of his teammates are zero. Therefore, Borr plus zero is +120, making Borr equal to +120.
You have to treat each player as if he’s his own universe, and you adjust for the extra parameters. The same logic applies to strength of schedule, or how to credit the DP between the 2B and SS, and several other concepts.
Splitting doesn’t work.
Let’s think about it in baseball terms then. Let’s say that you have Pedro v Pujols, and Pujols gets on base 4 times out of 10. So, that’s +0.6 above average. Would you split that, and give +0.3 to Pujols and +0.3 to Pedro. (Plus is good for the hitter and bad for the pitcher)? Let’s say we have Pedro v Chipper and Chipper gets on base 0 times out of 20. So, that’s -6.8 relative to average. Would you split that as -3.4 for Pedro and -3.4 for Chipper?
Suppose you say that “yes! that IS what I want to do”. Well, then you’ll end up with let’s say Pedro allowing 250 baserunners to 1000 batters faced. That total is -90 relative to average. In the 50/50 split, that means that if you add up each of the individual matchups (Pujols and Chipper and everyone else), then you end up with Pedro only being -45. Does that make sense? Well, no. His competition was already an average batter. He faced, as a group, hitters who get an OBP of .340. There’s no reason to try to do any kind of splitting the difference here.
Strat-O-Matic, for example, realizes this. (In Strat, they give 50% of the outcome to the batter’s card, and 50% to the pitcher’s card.) So, they give Pedro a rate of 80 baserunners per 500 batters faced, because they know in the other 500 batters faced that he has “no influence” on, he’s 170 / 500 (i.e., .340 OBP). The total is 250 baserunners per 1000 batters faced. If you focus on his 80/500, you realize that this is -90 relative to average.
This is why you can’t just “split the difference” IF AND ONLY IF the universe of your competition is indeed average to begin with. This is the idea behind WOWY (With Or Without You), and why it works well with pitchers/catchers at a career level, or pitchers/SS at a career level or in hockey or basketball on a seasonal level: those players face a diverse enough context that they can be said to be the sole driver to their performance (with some minor adjustments).
It fails in the case of say Mark Howe and Brad McCrimmon when they were +85 players that year, because they were defense partners on practically every shift. It fails for Dionne/Taylor/Simmer because they were out there as one unit for the Kings. It fails for Trammell/Whitaker, because they are always together.
This is why you have to be very careful. In some cases, you can do the 50/50, and in most cases, you should not.
Is this any clearer?
Friday, March 13, 2009
Boy, we’ve been giving Dewan a big platform these last two weeks! Here’s another. The writer, Geoff Baker (used to write for the Montreal Gazette) asked Dewan that since Dewan believes the Mariners upgraded their fielding by +2 wins, then…
Q: Could you qualify that? Does that mean that if they won 62 games last year, they’re going to win 64 this year? Dewan: “Yes. Yes.”
No. No.
No!
If you “show” you win 62 games in one season, this does not mean that you are a 62-win team. That is not the baseline you are working from. The actual baseline you are working from is whatever your talent base happens to be. And what that talent base did in their previous 162 games, as captured by the win-loss record, is not the true baseline. What that talent base did in their previous 162 games, as captured by OBP, SLG, PA, UZR, ERA, FIP, and alot of other component metrics is alot closer to the true baseline. And even then, it’s still not the true baseline.
Performance metrics are nothing more than samples. Just like when you run an experiment and you record what you see is just a sample of what the thing actually is. 162 games may sound like alot of trials, but it is not. And the reason that it is not is because all the teams are so close in talent anyway. If you have 30 coins, and each of them is weighted so that it will land heads a specific percentage of time, from a low of 45% to a high of 55%, do you really think that if you pick out one coin, and it lands on heads 40% of the time after 160 trials, that this means that the coin actually lands 40% of the time? Seeing that I’ve established that the minimum is 45% of the time (that’s your prior) then it becomes a simple Bayes problem to figure out the best estimate as to which of those 30 coins you’ve been flipping. There’s even a chance that you’ve been flipping the 55% coin!
So, no, no way, does it mean that if the Mariners stood pat that they’d win the same number of games, and no way does it mean that if the Mariners adds 2 wins in talent that they’d win 2 more games. That’s not how it works.
I don’t know if that quote captures the discussion between Geoff and John, and it’s not important for my purposes. What is important is that the answer quoted to the question quoted is 100% wrong.
(Hat tip: USSM)
Thursday, March 12, 2009
Sky gives us the breakdown. And, I must say, I love the presentation.
Friday, March 06, 2009
Joey M takes exception to Jay Jaffe, when Jay said:
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